,.,,., [1], [3], [4], [5] [6]. [2]. ,,, Tree-to-String. 1,f) input 1 (Python) : if x % 5 == 0: output 2 (Comment): # y x 5

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1 1,a) 2,b) 1,c) 1,d) 1,) 1,f) Tr-to-String 1.,.,,., [1], [2].,,,, 1.,,,. 1, 1, 1 Nara Institut of Scinc and Tchnology 2 Shinshu Univrsity a) b) c) d) ) f) input 1 (Python) : if x % 5 == 0: output 1 (Commnt): # x 5 input 2 (Python) : y = x / 5 output 2 (Commnt): # y x 5 1 Python., [3], [4], [5] [6].,., (, ),,.,,.,,,., c 2014 Information Procssing Socity of Japan 1

2 , Tr-to-String,.,, Tr-to-String.,. Python vr.3., [7], f ê (1). ê := arg max Pr( f) (1) f = f f 1 = [f 1, f 2,, f f ], = 1 = [ 1, 2,, ]. f., f ( ) [8], [9], Tr-to-String [10]. Tr-to-String,. 2.2 Tr-to-String f,. T f.,., [11]. Tr-to-String., (1), Tr-to-String. ê := arg max Pr( f) 2 = arg max arg max Tr-to-String Pr( f, T f )Pr(T f f) T f T f Pr( T f )Pr(T f f) (2) arg max Pr( Tf ) (3) Tr-to-String, 2,,. 2,., d. d, Tr-to-String. Pr( T f ), (4) d. d D Pr( T f ) := xp (w ϕ(t f,, d)) d D xp (w ϕ(t f,, d)) (4) D, ϕ d, w. ϕ,. Pr(T f f) f T f.,,. (3), Trto-String Tf = arg max Pr(T f f), T f., c 2014 Information Procssing Socity of Japan 2

3 ., (2) [12]. 2.3 Tr-to-String Tr-to-String (1), (2), (3) ,.., 2., [13], [14], [15] ,. GHKM [16]., ϕ, , w [17]. 3., Tr-to-String.,.,.,,., Tr-to-String. Tr-to-String. 3.1,.,. Python,., if,.,.,, 1, , Tr-to-String.,,. 3, if x is divisibl by 5 (a), Python if x % 5 == 0: (b). Ckylark *1, Python ast. x 5., Python. 3(b) Load,,. if,. Tr-to-String 2.3,,. 3,, 3(b)., Load,. *1 ckylark c 2014 Information Procssing Socity of Japan 3

4 1 Python : pass if a == b: if a == b:pass df foo(a, b, c): df foo(a, b, c):pass lif if Tru:pass\n lif foo(): if Tru:pass\nlif foo():pass ls ls: if Tru:pass\nls:pass xcpt try:pass\n xcpt: try:pass\nxcpt:pass 3,,.,.,, [18], [19], [20]., Tr-to-String,,.. 3.3, Python. (1), (2) 2, (3) (b) if,,., [21].,.,,. [21].,.,. 3(b) ,. 4,., c 2014 Information Procssing Socity of Japan 4

5 2 (Nam... (id (str x))...) (Nam x) (Num... (n (int x))...) (int x) (BinOp... (lft x) (op y) (right z)...) (BinOp (lft x) (right z) (op y)) (Compar... (lft x) (ops y) (comparators z)...) (Compar (lft x) (comparators z) (ops y)) 4 5,,.,., , (b) 5,, ,.,,. if x % 5 == 0: x 5, x 5,. 0,. 3,,.,. 6.,,. 6 x 5, 0, NAME1, INT1, INT2.,,.,,.,.,, Tr-to-String.. 3.4, 2.3 Tr-to-String c 2014 Information Procssing Socity of Japan 5

6 6,.. 4. : Python 4.1 Tr-to-String,. Python [22]. *2 Python 3., 722, Python, Tr-to-String 1,., ,., Tr-to-String. (1). (2) ( ),. *2 MCab[23]. MCab, ASCII,. foo _ bar 1-3. (3) Python 3.1,. Python ast. (4) (3), 3.3. (5) (2) (4),. (6) (5),. (invrsion transduction grammar: ITG) pialign[15]. ( ) 10.,, 80. (7) (5).,. n-gram KnLM[24], 5-gram. (8)Tr-to-String c 2014 Information Procssing Socity of Japan 6

7 7 Tr-to-String,,,, Tr-to-String. GHKM [16] Travatar[25].. 3 Accptability 5 (AA),. 4 (A),. 3 (B),. 2 (C),. 1 (F),. 4.3,.. raw,, Python ast. had,. rducd,,. constraind,.,,. 4.4,.,. BLEU[26] RIBES[27]. n-gram, BLEU, RIBES., 0, 1. Accptability[28]. 3 5, BLEU % RIBES % raw had rducd constraind Accptability Accptability raw had rducd constraind ,., 9, 1 10, 10. BLEU, RIBES rducd ( +), had ( ), raw ( ).,., constraind( ++).,. c 2014 Information Procssing Socity of Japan 7

8 5.2 8 Accptability 5 Accptability. 8, Accptability. Accptability,, rducd constraind 70%,. raw, Tr-to-String,., rducd, had, raw,., constraind rducd. constraind,,. 8, Accptability 5( ) 1().,, , 2 if x % 5 == 0: if x % 5 == 1:., 1 1, 0. 0.,,, Tr-to-String,.. constraind,.,.,,. 4, 5. constraind,,. Tr-to-String, Tr-to-String [12]. 7. Tr-to-String,.,., 1,.,.,,.,, Python.,., c 2014 Information Procssing Socity of Japan 8

9 Python if x % 5 == 0: if x % 5 == 1: raw x 5 x 5 1 had x 5 x 5 1 rducd x 5 x 5 1 constraind x 5 0 x [20],.,.,,.,,,.,. [1] DLin, R., Vnolia, G. and Rowan, K.: Softwar Dvlopmnt with Cod Maps, Commun. ACM, Vol. 53, No. 8, pp (2010). [2] Rahman, M. M. and Roy, C. K.: SurfClips: Contxt- Awar Mta Sarch in th IDE, Proc. ICSME, pp (2014). [3] Sridhara, G., Hill, E., Muppanni, D., Pollock, L. and Vijay-Shankr, K.: Towards automatically gnrating summary commnts for Java mthods, Proc. ASE, pp (2010). [4] Bus, R. P. and Wimr, W. R.: Automatic Documntation Infrnc for Excptions, Proc. ISSTA, pp (2008). [5] Sridhara, G., Pollock, L. and Vijay-Shankr, K.: Automatically Dtcting and Dscribing High Lvl Actions Within Mthods, Proc. ICSE, pp (2011). [6] Wong, E., Yang, J. and Tan, L.: AutoCommnt: Mining qustion and answr sits for automatic commnt gnration, Proc. ASE, pp (2013). [7] Brown, P. F., Pitra, V. J. D., Pitra, S. A. D. and Mrcr, R. L.: Th mathmatics of statistical machin translation: Paramtr stimation, Computational Linguistics, Vol. 19, No. 2, pp (1993). [8] Kohn, P., Och, F. J. and Marcu, D.: Statistical phras-basd translation, Proc. NAACL-HLT, pp (2003). [9] Chiang, D.: Hirarchical phras-basd translation, Computational Linguistics, Vol. 33, No. 2, pp (2007). [10] Huang, L., Knight, K. and Joshi, A.: Statistical syntaxdirctd translation with xtndd domain of locality, Proc. AMTA, Vol. 2006, pp (2006). [11] Nubig, G. and Duh, K.: On th Elmnts of an Accurat Tr-to-String Machin Translation Systm, Proc. ACL, Baltimor, USA (2014). [12] Mi, H., Huang, L. and Liu, Q.: Forst-Basd Translation, Proc. ACL-HLT, Columbus, Ohio, pp (2008). [13] Brown, P. F., Pitra, V. J. D., Pitra, S. A. D. and Mrcr, R. L.: Th Mathmatics of Statistical Machin Translation: Paramtr Estimation, Computational Linguistics, Vol. 19, No. 2, pp (1993). [14] Och, F. J. and Ny, H.: A Systmatic Comparison of Various Statistical Alignmnt Modls, Computational Linguistics, Vol. 29, No. 1, pp (2003). [15] Nubig, G., Watanab, T., Sumita, E., Mori, S. and Kawahara, T.: An Unsuprvisd Modl for Joint Phras Alignmnt and Extraction, Proc. ACL-HLT, Portland, Orgon, USA, pp (2011). [16] Gally, M., Hopkins, M., Knight, K. and Marcu, D.: What s in a Translation Rul?, Proc. NAACL-HLT, pp (2004). [17] Och, F. J.: Minimum Error Rat Training in Statistical Machin Translation, Proc. ACL, Sapporo, Japan, pp (2003). [18] Isozaki, H., Sudoh, K., Tsukada, H. and Duh, K.: Had Finalization: A Simpl Rordring Rul for SOV Languags, Proc. WMT, Uppsala, Swdn, pp (2010). [19] Hatakoshi, Y., Nubig, G., Sakti, S., Toda, T. and Nakamura, S.: Rul-basd Syntactic Prprocssing for Syntax-basd Machin Translation, Proc. SSST, Doha, Qatar, pp (2014). [20] Burktt, D. and Klin, D.: Transforming Trs to Improv Syntactic Convrgnc, Proc. EMNLP, Jju Island, South Kora (2012). [21] Li, P., Liu, Y. and Sun, M.: An Extndd GHKM Algorithm for Inducing λ-scfg, Proc. AAAI (2013). [22] Vol. 2014, No. 22, pp. 1 8 (2014). [23] Kudo, T., Yamamoto, K. and Matsumoto, Y.: Applying Conditional Random Filds to Japans Morphological Analysis., Proc. EMNLP, Vol. 4, pp (2004). [24] Hafild, K., Pouzyrvsky, I., Clark, J. H. and Kohn, P.: Scalabl Modifid Knsr-Ny Languag Modl Estimation, Proc. ACL, Sofia, Bulgaria, pp (2013). [25] Nubig, G.: Travatar: A Forst-to-String Machin Translation Engin basd on Tr Transducrs, Proc. ACL, Sofia, Bulgaria, pp (2013). [26] Papinni, K., Roukos, S., Ward, T. and Zhu, W.-J.: BLEU: A Mthod for Automatic Evaluation of Machin Translation, Proc. ACL, pp (2002). [27] Isozaki, H., Hirao, T., Duh, K., Sudoh, K. and Tsukada, H.: Automatic Evaluation of Translation Quality for Distant Languag Pairs, Proc. EMNLP, pp (2010). [28] Goto, I., Chow, K. P., Lu, B., Sumita, E. and Tsou, B. K.: Ovrviw of th patnt machin translation task at th NTCIR-10 workshop, NTCIR-10 (2013). c 2014 Information Procssing Socity of Japan 9